We study symmetry properties of bivariate copulas. For this, we introduce an order of asymmetry, as well as measures of asymmetry which are monotone in that order. In an empirical study, we illustrate that asymmetric dependence structures do indeed occur in financial market data and discuss its...

We propose to forecast the Value-at-Risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence. We compare our proposed method to a conventional copula-GARCH model where the parameter of a Clayton...

The purpose of this paper is to present a comprehensive simulation study on the finite sample properties of minimum-distance and maximum-likelihood estimators for bivariate and multivariate parametric copulas. For five popular parametric copulas, classical maximum-likelihood is compared to a...

We propose the use of nonparametric Bernstein copulas as bivariate pair-copulas in high-dimensional vine models. The resulting smooth and nonparametric vine copulas completely obviate the error-prone need for choosing the pair-copulas from parametric copula families. By means of a simulation...

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both...

This paper analyzes the systemic risk effects of bank mergers to test the "concentration-fragility" hypothesis. We use the marginal expected shortfall as well as the lower tail dependence between a bank's stock returns and a relevant bank sector index to capture the merger-related change in an...